Parameters ———- data : array_like Input data. mask : sequence, optional Mask. Must be convertible to an array of booleans with the same shape as `data`. True indicates a masked (i.e. invalid) data. fill_value : scalar, optional Value used to fill in the masked values when necess...
Mask. Must be convertible to an array of booleanswiththe same shapeas`data`.Trueindicates amasked(i.e. invalid)data. fill_value : scalar, optional Value used to fillinthe masked values when necessary. IfNone, a default based on the data-typeisused. data就不多说了,一个array_like,tuple...
def annToRLE(self, ann, height, width): """ 转化多边形的标注, 未压缩的RLE转成RLE. :返回:二值mask (numpy 2D array) """ segm = ann['segmentation'] if isinstance(segm, list): # 多边形 -- 一个物体可能由多个部分构成 # 将所有部分合并到一个mask rle code rles = maskUtils.frPyObjects...
scales: 1D array of anchor sizes in pixels. Example: [32, 64, 128] ratios: 1D array of anchor ratios of width/height. Example: [0.5, 1, 2] shape: [height, width] spatial shape of the feature map over which to generate anchors. feature_stride: Stride of the feature map relative to...
[input_image, input_image_meta, input_anchors], [detections, mrcnn_class, mrcnn_bbox, mrcnn_mask, rpn_rois, rpn_class, rpn_bbox], name='mask_rcnn') return model def get_train_model(config): # Image size must be dividable by 2 multiple times h, w = config.IMAGE_SHAPE[:2] ...
"After resizing, all images must have the same size. Check IMAGE_RESIZE_MODE and image sizes." 简单的纠错和日志控制之后,即调用mold_input函数对输入图片进行调整,并记录图片信息。 self.mold_inputs方法如下, 1 2 3 4 5 6 7 8 9 10
def log(text, array=None): """Prints a text message. And, optionally, if a Numpy array is provided it prints it's shape, min, and max values. """ if array is not None: text = text.ljust(25) text += ("shape: {:20} ".format(str(array.shape))) if array.size:...
"""Load the specified image and return a [H,W,3] Numpy array. """ # Load image image = skimage.io.imread(self.image_info[image_id]['path']) # If grayscale. Convert to RGB for consistency. if image.ndim != 3: image = skimage.color.gray2rgb(image) ...
首先,源码来源: https://github.com/matterport/Mask_RCNNgithub.com/matterport/Mask_RCNN 实验所用数据集为3类缺陷:划痕、凸起、凹坑 标注格式:VGG Image Annotator(VIA) 这里注意:我这里用的是1.0.6版本,因为涉及到后续获取json文件中的关键字段读取,所以尽量保持版本一致减少麻烦。
Returns: bbox array [num_instances, (y1, x1, y2, x2)].""" boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32) for i in range(mask.shape[-1]): m = mask[:, :, i] # Bounding box.horizontal_indicies = np.where(np.any(m, axis=0))[0] ...